\section{Known Issues}
\paragraph{distpy}
has been designed for deployment across the widest possible range of platforms, from Edge to Cloud to on-prem to laptop. 
It can be used from within your favourite Python environment. Some Python environment are not, however, suited for large-scale processing. 
 
\paragraph{Anaconda environments} are generally a great Python installation for distpy, howevever, the Spyder debugger delivered with 
Anaconda will hold onto variables to aid in any debugging. 
When processing large datasets this can lead to an accumulation of stored memory that 
can eventually cause your machine to run out of memory. 
The best option here is to develop your script and configuration within the Spyder environment, 
but then to launch your full-scale processing in the Anaconda Prompt.
The command is:
\begin{lstlisting}
python -m myscript
\end{lstlisting}
 
\paragraph{Schlumberger's Techlog}
Python Environment is not suited to running parallel. 
You can design your processing flow using a small subset of the data running serial processing, 
but then launch your full-scale processing using the Techlog python executable. This will typically be:
\begin{lstlisting}
"C:\Program Files\Schlumberger\Techlog 2019.1 (r23821)\Python36_x64\python.exe" -m myscript
\end{lstlisting}

